



'''simple docstring'''






from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """EncodecFeatureExtractor"""
    lowercase__					=       ("""T5Tokenizer""", """T5TokenizerFast""")







    def __init__( self					:    Tuple		,     __snake_case					:    Any		,     __snake_case					:    int      ):
      super().__init__(__snake_case		,     __snake_case      )
      a		:   Dict	  =	self.feature_extractor
      a		:   Optional[int]	  =	False







    def      lowercase_			( self					:    Any		,     __snake_case					:    Optional[Any]=None		,     __snake_case					:    int=None		,     __snake_case					:    Optional[int]=True      ):
      return self.tokenizer.get_decoder_prompt_ids(task=__snake_case		,     language=__snake_case		,     no_timestamps=__snake_case      )







    def __call__( self					:    Tuple		,     *__snake_case					:    Tuple		,     **__snake_case					:    int      ):
      # For backward compatibility
      if self._in_target_context_manager:
        return self.current_processor(*__snake_case		,     **__snake_case      )

      a		:   List[str]	  =	kwargs.pop('audio'		,     __snake_case      )
      a		:   Tuple	  =	kwargs.pop('sampling_rate'		,     __snake_case      )
      a		:   Tuple	  =	kwargs.pop('text'		,     __snake_case      )
      if len(__snake_case      ) > 0:
        a		:   Optional[Any]	  =	args[0]
        a		:   str	  =	args[1:]

      if audio is None and text is None:
        raise ValueError('You need to specify either an `audio` or `text` input to process.'      )

      if text is not None:
        a		:   Union[str, Any]	  =	self.tokenizer(__snake_case		,     **__snake_case      )

      if audio is not None:
        a		:   Tuple	  =	self.feature_extractor(__snake_case		,     *__snake_case		,     sampling_rate=__snake_case		,     **__snake_case      )

      if audio is None:
        return inputs

      elif text is None:
        return audio_inputs

      else:
        a		:   Union[str, Any]	  =	audio_inputs['input_values']
        if "padding_mask" in audio_inputs:
          a		:   Dict	  =	audio_inputs['padding_mask']
        return inputs







    def      lowercase_			( self					:    Union[str, Any]		,     *__snake_case					:    Any		,     **__snake_case					:    Tuple      ):
      a		:   str	  =	kwargs.pop('audio'		,     __snake_case      )
      a		:   Optional[int]	  =	kwargs.pop('padding_mask'		,     __snake_case      )

      if len(__snake_case      ) > 0:
        a		:   List[str]	  =	args[0]
        a		:   Dict	  =	args[1:]

      if audio_values is not None:
        return self._decode_audio(__snake_case		,     padding_mask=__snake_case      )
      else:
        return self.tokenizer.batch_decode(*__snake_case		,     **__snake_case      )







    def      lowercase_			( self					:    Tuple		,     *__snake_case					:    List[Any]		,     **__snake_case					:    Optional[int]      ):
      return self.tokenizer.decode(*__snake_case		,     **__snake_case      )







    def      lowercase_			( self					:    Optional[Any]		,     __snake_case					:    Union[str, Any]		,     __snake_case					:    Optional = None      ):
      a		:   str	  =	to_numpy(__snake_case      )
      a  ,      a  ,      a		:   Any	  =	audio_values.shape

      if padding_mask is None:
        return list(__snake_case      )

      a		:   int	  =	to_numpy(__snake_case      )

      # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
      # token (so that the generated audio values are **not** treated as padded tokens)
      a		:   Tuple	  =	seq_len - padding_mask.shape[-1]
      a		:   int	  =	1 - self.feature_extractor.padding_value
      a		:   str	  =	np.pad(__snake_case		,     ((0, 0), (0, difference))		,     'constant'		,     constant_values=__snake_case      )

      a		:   Any	  =	audio_values.tolist()
      for i in range(__snake_case      ):
        a		:   List[str]	  =	np.asarray(audio_values[i]      )[
            padding_mask[i][None, :] != self.feature_extractor.padding_value
        ]
        a		:   Optional[Any]	  =	sliced_audio.reshape(__snake_case		,     -1      )

      return audio_values




'''simple docstring'''






from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    int		,     __snake_case					:    Callable		,     __snake_case					:    Optional[Features] = None		,     __snake_case					:    str = None		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    Optional[dict] = None		,     __snake_case					:    Optional[int] = None		,     **__snake_case					:    Optional[int]		,     ):
      super().__init__(
          features=__snake_case		,     cache_dir=__snake_case		,     keep_in_memory=__snake_case		,     streaming=__snake_case		,     num_proc=__snake_case		,     **__snake_case		,     )
      a		:   List[Any]	  =	Generator(
          cache_dir=__snake_case		,     features=__snake_case		,     generator=__snake_case		,     gen_kwargs=__snake_case		,     **__snake_case		,     )







    def      lowercase_			( self					:    Any      ):
      # Build iterable dataset
      if self.streaming:
        a		:   Optional[int]	  =	self.builder.as_streaming_dataset(split='train'      )
      # Build regular (map-style) dataset
      else:
        a		:   List[Any]	  =	None
        a		:   Any	  =	None
        a		:   Optional[int]	  =	None
        a		:   List[str]	  =	None

        self.builder.download_and_prepare(
            download_config=__snake_case		,     download_mode=__snake_case		,     verification_mode=__snake_case		,     base_path=__snake_case		,     num_proc=self.num_proc		,     )
        a		:   Dict	  =	self.builder.as_dataset(
            split='train'		,     verification_mode=__snake_case		,     in_memory=self.keep_in_memory      )
      return dataset